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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Jurnal Teknoinfo JURTEKSI Jurnal Tekno Kompak JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Algoritma Journal of Engineering and Information Technology for Community Service Jurnal Komputasi
Zaenal Abidin
Universitas Teknokrat Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Efek Peningkatan Jumlah Paralel Korpus Pada Penerjemahan Kalimat Bahasa Indonesia ke Bahasa Lampung Dialek Api Permata Permata; Zaenal Abidin; Farida Ariyani
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2613

Abstract

Experimental observations of the effect of the number of parallel corpus on Indonesian translation into the Lampung dialect api were carried out using the statistical machine translation (SMT) method. SMT utilizes a parallel Indonesian corpus and its translation in the Lampung dialect api as a material for training data. The research strategy was carried out in three ways, namely first strategy with a corpus parallel number of 1000 sentences, the second strategy with a corpus parallel number of 2000 and the third strategy with a corpus parallel number of 3000 sentences. The research starts from the preprocessing phase followed by the training phase, namely the parallel corpus processing phase to obtain a language model and translation model. Then the testing phase, and ends with the evaluation phase. SMT testing uses 25 single sentences without out-of-vocabulary (OOV), 25 single sentences with OOV, 25 compound sentences without OOV and 25 compound sentences with OOV. The test results of translating Indonesian sentences intoLampung dialectic api are shown through the accuracy value of Bilingual Evaluation Undestudy (BLEU) obtained in testing 25 single sentences without out-of-vocabulary (OOV) in the first strategy, the second and the third are 21.49%, 59.58% and 73.21%. In testing 25 single sentences with out-of-vocabulary (OOV) obtained in the first strategy, the second and the third are 23.22%, 44.33% and 68.72%. In testing 25 compound sentences without out-of-vocabulary(OOV) obtained in the first strategy, the second and the third are 18.22%, 39.4% and 69.18%. In testing 25 compound sentences with out-of-vocabulary (OOV) obtained in the first strategy, the second and the third are 25.94%, 28.22% and 71.94%.
Prediksi Penyakit Asma Menggunakan Naïve Bayes dan Random Forest Berbasis Data Klinis Multivariat Salsabila Ainur Hidayah; Zaenal Abidin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3521

Abstract

Asma merupakan penyakit kronis pada sistem pernapasan yang berpotensi menurunkan kualitas hidup pasien secara signifikan apabila tidak terdeteksi dan ditangani secara tepat. Penelitian ini bertujuan membandingkan kinerja Gaussian Naïve Bayes dan Random Forest dalam prediksi penyakit asma menggunakan Asthma Disease Dataset dari Kaggle yang memuat 2.392 data pasien dengan distribusi kelas yang tidak seimbang, yakni 2.268 data non-asma dan 124 data asma. Tahapan yang ditempuh meliputi seleksi fitur, preprocessing menggunakan StandardScaler, penanganan ketidakseimbangan kelas dengan SMOTE yang diterapkan eksklusif pada data latih, proses klasifikasi, serta evaluasi model menggunakan metrik accuracy, precision, recall, F1-score, ROC AUC, Cohen's Kappa, MCC, dan 5-Fold Cross Validation. Hasil pengujian menunjukkan bahwa Random Forest memperoleh accuracy tertinggi sebesar 0,904 dengan precision 0,080, sedangkan Gaussian Naïve Bayes menghasilkan recall 0,520, F1-score 0,134, dan ROC AUC 0,638. Temuan ini mengindikasikan bahwa Random Forest lebih unggul dari sisi akurasi keseluruhan, sementara Gaussian Naïve Bayes lebih efektif dalam mendeteksi kasus asma pada dataset yang digunakan. Hasil penelitian ini dapat menjadi referensi dalam pengembangan sistem pendukung keputusan untuk identifikasi risiko asma, meskipun validasi lebih lanjut menggunakan data klinis yang lebih beragam tetap diperlukan.
Co-Authors Ade Surahman Agung Setiawan Ahmad Syihabuddin Aldi Wijaya Andi Nurkholis Anugrayani Bustamin Arliyanti Nurdin Arsya Kharisma Amartya Bernadus Anggo Seno AjiAji Dedi Darwis Diah Ambarwati Dina Amelia Donaya Pasha Eka Nurhana Endang Hidayat Farida Ariyani Faruq Ulum Gunawan, Rakhmat Dedi Heni Sulistiani Isnain, Auliya Rahman Muhamad Odhie Prasetio Nery Siskawati Permata Permata Permata Permata Putri Nurlandari Rachmi Marsheilla Aguss Riduwan Napianto Rohmat Indra Borman Salsabila Ainur Hidayah Styawati Styawati Taufik Ridwan, Taufik Theo Kusuma Pamungkas Ulum, Faruk Zulfa Nabila